Simulation and Implementation of a Morphology-Tuned Gold Nano-Islands Integrated Plasmonic Sensor
Bibliographic record
Abstract
This work presents simulation, analysis and implementation of morphology tuning of gold nano-island structures deposited by a novel convective assembly technique. The gold nano-islands were simulated using 3D Finite-Difference Time-Domain (FDTD) techniques to investigate the effect of morphological changes and adsorption of protein layers on the localized surface plasmon resonance (LSPR) properties. Gold nano-island structures were deposited on glass substrates by a novel and low-cost convective assembly process. The structure formed by an uncontrolled deposition method resulted in a nano-cluster morphology, which was annealed at various temperatures to tune the optical absorbance properties by transforming the nano-clusters to a nano-island morphology by modifying the structural shape and interparticle separation distances. The dependence of the size and the interparticle separation distance of the nano-islands on the LSPR properties were analyzed in the simulation. The effect of adsorption of protein layer on the nano-island structures was simulated and a relation between the thickness and the refractive index of the protein layer on the LSPR peak was presented. Further, the sensitivity of the gold nano-island integrated sensor against refractive index was computed and compared with the experimental results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".